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        <h1>DeepSEE:<br>Deep Disentangled Semantic<br>Explorative Extreme
            Super-Resolution</h1>

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        <h4>
            <a href="http://www.mcbuehler.ch"
               target="_blank">Marcel C. Bühler</a>&nbsp;&nbsp;&nbsp;
            <a href="https://ee.ethz.ch/the-department/people-a-z/person-detail.MjQ5ODc2.TGlzdC8zMjc5LC0xNjUwNTg5ODIw.html"
               target="_blank">Andrés Romero</a>&nbsp;&nbsp;&nbsp;
            <a href="https://people.ee.ethz.ch/~timofter/" target="_blank">Radu
                Timofte</a>&nbsp;&nbsp;&nbsp;
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        <a href="https://vision.ee.ethz.ch/" target="_blank">Computer
            Vision Lab</a>,
        <a href="https://ethz.ch/" target="_blank">ETH Zurich</a>&nbsp;&nbsp;&nbsp;
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        <div class="col-md-12"><h4>
        in <a href="http://accv2020.kyoto/" title="ACCV 2020" target="_blank">ACCV 2020</a> (oral)&nbsp;&nbsp;&nbsp;
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        <h4><strong><a target="_blank" title="Main paper" href="https://openaccess.thecvf.com/content/ACCV2020/html/Buhler_DeepSEE_Deep_Disentangled_Semantic_Explorative_Extreme_Super-Resolution_ACCV_2020_paper.html">Paper</a> |
            <a target="_blank" title="Supplementary material" href="https://openaccess.thecvf.com/content/ACCV2020/supplemental/Buhler_DeepSEE_Deep_Disentangled_ACCV_2020_supplemental.PDF">Supplementary material</a> |
            <a target="_blank" title="GitHub" href="https://github.com/mcbuehler/DeepSEE">GitHub</a> |
            <a target="_blank" title="Youtube" href="https://youtu.be/WW1ehTAzI7k">Youtube</a>

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        <img src="images/deepsee_main.gif" width="97%">
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        <h2 align="center">Abstract</h2>

        <div style="font-size:14px"><p align="justify">
            <p align="justify"><i>Super-resolution (SR) is by definition ill-posed. There are infinitely many plausible high-resolution variants for a given low-resolution natural image. Most of the current literature aims at a single deterministic solution of either high reconstruction fidelity or photo-realistic perceptual quality. In this work, we propose an explorative facial super-resolution framework, DeepSEE, for Deep disentangled Semantic Explorative Extreme super-resolution. To the best of our knowledge, DeepSEE is the first method to leverage semantic maps for explorative super-resolution. In particular, it provides control of the semantic regions, their disentangled appearance and it allows a broad range of image manipulations. We validate DeepSEE on faces, for up to 32x magnification and exploration of the space of super-resolution.</i></p>
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            <a href="https://arxiv.org/abs/2004.04433"><img
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                    alt="paper thumbnail" src="images/paper_thumbnail.jpg"
                    width=50%></a>
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            <h2>Paper</h2>
            <p>
                <a target="_blank" title="Main paper" href="https://arxiv.org/abs/2004.04433">Main paper (~9 MB)</a></p>
            <p>
                <a target="_blank" title="Supplementary material" href="https://drive.google.com/file/d/1vRdmV1zL5YgyMeH9moDq36qnKeG7CBjT/view?usp=sharing">Supplementary material (~20 MB)</a></p>

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        <h2>Visual Results</h2>
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                <a href="images/deepsee_semantic_manipulations_256x256.gif"><img
                        src="images/deepsee_semantic_manipulations_128x128.gif"
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                <h4>Semantic Manipulations</h4>
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                <a href="images/deepsee_style_manipulations_256x256.gif"><img
                        src="images/deepsee_style_manipulations_128x128.gif"
                > </a>
                <h4>Style Manipulations</h4>
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                <a href="images/deepsee_extreme_512x512.png"><img
                        src="images/deepsee_extreme_light.png">
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                <h4>Extreme Upscaling (32x)</h4>
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        <h1 align='center'>Method</h1>
        <img src="images/deepsee_method.png" width="90%">
        <p align="justify">
            Our generator upscales low-resolution input (LR) conditioned on
            both a semantic layout and a style matrix. This allows to
            control
            the appearance, as well as the size and shape of each region in
            the
            semantic layout. By modifying these conditional inputs, DeepSEE can
            generate a multitude of potential solutions and
            explore the solution space.
        </p>
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        <h1 align='center'>Code and Models</h1>
        <p align="justify">Please check out our <a href="https://github.com/mcbuehler/DeepSEE"> GitHub repository</a>. </p>
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        <h2>Citation</h2>
        <p>Marcel Christoph Bühler, Andrés Romero, and Radu Timofte. <br>
            Deepsee: Deep disentangled semantic explorative extreme super-resolution. <br>
            In The 15th Asian Conference on Computer Vision (ACCV), 2020.
            <a href="citation.txt">Bibtex</a></p>
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        <h2>Acknowledgements</h2>
        <p align="justify">We would like to thank the
            <a href="https://haslerstiftung.ch/en" target="_blank" title="Hasler Foundation"> Hasler Foundation</a>.
            In addition, this work was partly supported by the
            <a href="https://www.ethz-foundation.ch/en/" target="_blank" title="ETH Zürich
                Fund">ETH Zürich Fund (OK)</a>, a <a href="https://www.huawei.com/us/" target="_blank" title="Huawei Technologies">Huawei Technologies Oy</a> (Finland)
            project, an
            <a href="https://aws.amazon.com/" target="_blank" title="Amazon AWS">Amazon AWS</a> and an <a href="https://www.nvidia.com" target="_blank" title="NVIDIA">NVIDIA</a> grant.</p>
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